Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem

TL;DR: This paper proposes a Greedy Search based Ant Colony Optimization (GSACO) algorithm for large-scale semiconductor production scheduling, outperforming exact optimization methods on classical and large-scale benchmarks with up to 10,000 operations.

TL;DR: In this paper , the authors present the research background and current state of distributed shop scheduling and discuss hot topics and concerns in this field. And they provide an overview of research methods and objects, highlighting the development of solution methods and research trends.

TL;DR: This paper sets out to encode the scheduling problem into propositional Boolean logic and determine the optimal makespan by repeatedly calling an off-the-shelf SAT solver, and moves one step further by encoding the problem into Partial Maximum Satisfiability (PMS), which is an optimized version of SAT, so that the explicit successive calls of the solver can be eliminated.

TL;DR: This formulation of discrete linear programming seems, however, to involve considerably fewer variables than two other recent proposals and on these grounds may be worth some computer experimentation.

TL;DR: The goal of this paper is to develop models and methods that use complementary strengths of Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) techniques to solve problems that are otherwise intractable if solved using either of the two methods.

TL;DR: A mathematical model and heuristic approaches for flexible job shop scheduling problems (FJSP) are considered and it is concluded that the hierarchical algorithms have better performance than integrated algorithms and the algorithm which use tabu search and simulated annealing heuristics for assignment and sequencing problems consecutively is more suitable than the other algorithms.

TL;DR: After an exhaustive computational and statistical analysis it can be concluded that the proposed method shows an excellent performance overcoming the rest of the evaluated methods in a comprehensive benchmark set of instances.